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Abstract Valproic acid (VPA) is one of the most effective antiepileptic drugs, and exposing animals to VPA during gestation has been used as a model for autism spectrum disorder (ASD). Numerous studies have shown that impaired synaptic transmission in the cerebellar cortical circuits is one of the reasons for the social deficits and repetitive behavior seen in ASD. In this study, we investigated the effect of VPA exposure during pregnancy on tactile stimulation-evoked cerebellar mossy fiber-granule cell (MF-GC) synaptic transmission in mice anesthetized with urethane. Three-chamber testing showed that mice exposed to VPA mice exhibited a significant reduction in social interaction compared with the control group. In vivo electrophysiological recordings revealed that a pair of air-puff stimulation on ipsilateral whisker pad evoked MF-GC synaptic transmission, N1, and N2. The evoked MF-GC synaptic responses in VPA-exposed mice exhibited a significant increase in the area under the curve (AUC) of N1 and the amplitude and AUC of N2 compared with untreated mice. Cerebellar surface application of the selective N-methyl-D-aspartate (NMDA) receptor blocker D-APV significantly inhibited facial stimulation-evoked MF-GC synaptic transmission. In the presence of D-APV, there were no significant differences between the AUC of N1 and the amplitude and AUC of N2 in the VPA-exposed mice and those of the untreated mice. Notably, blockade of the GluN2A subunit-containing, but not the GluN2B subunit-containing, NMDA receptor, significantly inhibited MF-GC synaptic transmission and decreased the AUC of N1 and the amplitude and AUC of N2 in VPA-exposed mice to levels similar to those seen in untreated mice. In addition, the GluN2A subunit-containing NMDA receptor was expressed at higher levels in the GC layer of VPA-treated mice than in control mice. These results indicate that gestational VPA exposure in mice produces ASD-like behaviors, accompanied by increased cerebellar MF-GC synaptic transmission and an increase in GluN2A subunit-containing NMDA receptor expression in the offspring.

C-arm computed tomography (CT)-guided percutaneous balloon compression (PBC) achieves intraoperative visualization, which allows for an intuitive display of the morphology and positional relationship between the balloon and the cranial base osseous structures. By promptly diagnosing variations or abnormalities in the cranial base structures and adjusting surgical strategies in a timely manner during the operation, precision treatment can be achieved. We report a rare case of diagnosing dural calcification in the cranial base during C-arm CT-guided PBC for trigeminal neuralgia.

External sulfate attack (ESA) coupled with wetting–drying alternation poses a significant threat to the concrete durability, while there are few models to simulate the above ESA behavior. In this paper, a simplified transport-mechanical model is proposed to numerically analyze the deterioration of concrete prism under ESA and dry-wet cycles. Firstly, a transport model of sulfate ion in concrete prism is established by considering the behaviors of ion diffusion, convection and chemical reaction, and numerically solved by the finite difference method. Secondly, the crystallization pressure of sulfate products in porous system is analyzed to calculate the equivalent expansion force. Then, the expansion strain and its-induced damage are calculated, and the criterion of boundary movement is further proposed. On the basis of model validation, a numerical simulation is performed to analyze the transport-mechanical response in concrete prism under ESA and dry-wet cycles. The results show that, there is no clear demarcation between the convection and diffusion zones in the prism. The spatial–temporal evolution of ESA-induced damage is similar to that of ion concentration. The longer the wetting time in one dry-wet cycles, the deeper the spalling of concrete prism caused by ESA.

Objective: To construct the deep learning convolution neural network (CNN) model and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accuracy of image diagnosis., Methods: Maxillary sinus CT data of 1000 samples in 500 patients from January 2018 to December 2021 in our hospital was collected. The first part is the establishment and testing of chronic maxillary sinusitis detection model by 461 images. The second part is the establishment and testing of the detection model of chronic maxillary sinusitis with bone remodeling by 802 images. The sensitivity, specificity and accuracy and area under the curve (AUC) value of the test set were recorded, respectively., Results: Preliminary application results of CT based AI in the diagnosis of chronic maxillary sinusitis and bone remodeling. The sensitivity, specificity and accuracy of the test set of 93 samples of CMS, were 0.9796, 0.8636 and 0.9247, respectively. Simultaneously, the value of AUC was 0.94. And the sensitivity, specificity and accuracy of the test set of 161 samples of CMS with bone remodeling were 0.7353, 0.9685 and 0.9193, respectively. Simultaneously, the value of AUC was 0.89., Conclusion: It is feasible to use artificial intelligence research methods such as deep learning and machine learning to automatically identify CMS and bone remodeling in MSCT images of paranasal sinuses, which is helpful to standardize imaging diagnosis and meet the needs of clinical application., (© 2024. The Author(s).)